Abstract
We propose a method for audio event detection in video streams from news. Apart from detecting speech, which is obviously the major class in such content, the proposed method detects five non-speech audio classes. The major difficulty of the particular task lies in the fact that most of the non-speech audio events are actually background sounds, with speech as the primary sound. We have adopted a set of 21 statistics computed on a mid-term basis over 7 audio features. A variation of the One Vs All classification architecture has been adopted and each binary classification problem is modeled using a separate probabilistic Support Vector Machine. Experiments have shown that the proposed method can achieve high precision rates for most of the audio events of interest.
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© 2010 Springer-Verlag Berlin Heidelberg
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Petridis, S., Giannakopoulos, T., Perantonis, S. (2010). A Multi-class Method for Detecting Audio Events in News Broadcasts. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_50
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DOI: https://doi.org/10.1007/978-3-642-12842-4_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12841-7
Online ISBN: 978-3-642-12842-4
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